PROJECT SUMMARY Molecular imaging has been a dream of biomedical imaging scientists for decades, and governments and industries around the world have invested billions in this area. However, most existing molecular imaging techniques require exogenous molecular probes or reporters to be introduced into a subject in order to obtain molecule-specific information, thereby limiting their practical utility. Magnetic resonance spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for non-invasive, label-free molecular imaging. Proton (1H) MRSI, in particular, allows for the observation, identification and quantification of a large number of biologically important molecules (neurotransmitters and metabolites), and provides a unique capability to study brain metabolism and neurodegenerative diseases. However, clinical and research applications of 1H-MRSI have been developing very slowly due to several long-standing technical barriers, including long data acquisition time, poor spatial resolution, low signal-to-noise (SNR), and overwhelming nuisance signals. The primary objective of the proposed project is to bring a new MRSI technology, known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE), into practical use for rapid high-resolution metabolic imaging of the brain. SPICE is based on a new approach to MRSI, which contains several key innovative features, including combined ultrashort-TE/short-TR acquisitions without water and lipid suppression, rapid sampling of (k, t)-space, constrained image reconstruction, and statistical spectral quantification using both physics-based spectral bases and spatial constraints. Our preliminary results have shown an exciting potential of SPICE to achieve an unprecedented combination of resolution, speed, and SNR for MRSI. This project will further develop and optimize SPICE data acquisition and processing and validate its performance to lay a solid foundation for clinical investigation. We believe that SPICE, when fully developed, will transform noninvasive, label- free metabolic imaging of the brain.